09 - RAG in PHP - Answer From Your Own Docs, Not the Model's Memory

The failure mode you hit the moment you point an LLM at internal questions: it hallucinates policy that sounds right but was never written. "Can I work from Hawaii for two months?" is exactly the kind of question where a confident wrong answer costs you real money.

Retrieval-Augmented Generation (RAG) fixes it: retrieve the relevant doc first, then answer from that - and say so when there's no answer. In NanoAgent, RAG is just a search tool. No vector database required to start.

The knowledge base (a plain array here)

$knowledgeBase = [
    'policy_wfh' => [
        'title' => 'Remote Work Policy 2024',
        'content' => 'Employees may work remotely up to 3 days a week. Full remote work '
                    . 'requires Director approval. Working from international locations is '
                    . 'limited to 30 days per year due to tax implications.'
    ],
    'policy_holiday' => [
        'title' => 'Holiday Schedule 2024',
        'content' => 'Closed on New Year\'s Day, Memorial Day, Independence Day, Labor Day, '
                    . 'Thanksgiving, and Christmas Day.'
    ],
    'it_support' => [
        'title' => 'IT Support Contacts',
        'content' => 'Urgent: ext 5555. Non-urgent: [e-mail]. Password resets via portal.'
    ]
];

The search tool

The tool takes one argument - free-text keywords - and describes itself as a policy search, so the model knows to reach for it on anything HR-shaped.

use NanoAgent\Agent;
use NanoAgent\Tools\FunctionTool;

$searchTool = new FunctionTool(
    name: 'search_knowledge_base',
    description: 'Searches the internal knowledge base for policy documents. Input keywords.',
    parameters: [
        'type' => 'object',
        'properties' => [
            'query' => ['type' => 'string', 'description' => 'Keywords to search for']
        ],
        'required' => ['query']
    ],

The callable does a plain case-insensitive substring match against title and content, and returns either the matching documents or an explicit "nothing found" - never a guess.

    callable: function (array $args) use ($knowledgeBase) {
        $q = strtolower($args['query']);
        $hits = [];
        foreach ($knowledgeBase as $doc) {
            if (str_contains(strtolower($doc['title']), $q)
                || str_contains(strtolower($doc['content']), $q)) {
                $hits[] = "Title: {$doc['title']}\nContent: {$doc['content']}";
            }
        }
        return $hits
             ? implode("\n\n---\n\n", $hits)
             : "No relevant documents found.";
    }
);

The agent - and the prompt that makes RAG safe

$agent = new Agent(
    llm: $llmConfig,
    systemPrompt: "You are a professional HR assistant. You must ONLY answer based on the "
                . "search results. If the information is not present, politely say so. "
                . "Always cite the document title.",
    tools: [$searchTool]
);

$response = $agent->chat("Can I work from Hawaii for two months?");
echo $response;

That last prompt line is the whole safety model. Three rules do the heavy lifting:

1. "ONLY answer based on the search results" - no training-data memory allowed.
2. "If not present, say so" - the model is permitted to decline, which is what stops the confident hallucination.
3. "Cite the document title" - every claim is traceable to a source.

For the Hawaii question, the tool retrieves Remote Work Policy 2024 (it contains "international locations"), and the agent answers: limited to 30 days/year, so two months isn't allowed - per Remote Work Policy 2024. Grounded, cited, correct.

From array to real RAG - the same seam

The only thing that changes as you scale is what the callable does:

Stage	The search_knowledge_base callable does
Demo	keyword scan of a PHP array
Real (small)	query your SQL/Postgres docs table
Real (large)	embed the query + docs, hit a vector store (Qdrant, pgvector)

The agent code never changes. You keep the same tool name and the same prompt; you just make retrieval smarter inside the function. Start with the dumb keyword search - it's enough to prove the pattern and to handle small corpora - and upgrade the retrieval when you need to.

Why tools (not pre-prompting) for RAG

You could stuff the whole doc into the prompt. That blows the context window and wastes tokens on irrelevant content. A tool lets the model decide what to retrieve and when, pulling only what's relevant. As your corpus grows, that's the difference between "works" and "doesn't fit."

---

Part of the NanoAgent examples series. Landing + demos.

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